{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#本章需导入的模块\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(action = 'ignore')\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.sans-serif']=['SimHei']  #解决中文显示乱码问题\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "from sklearn.model_selection import train_test_split,KFold,cross_val_score\n",
    "from sklearn import tree\n",
    "import sklearn.linear_model as LM\n",
    "from sklearn import ensemble\n",
    "from sklearn.datasets import make_classification,make_circles,make_regression\n",
    "from sklearn.metrics import zero_one_loss,r2_score,mean_squared_error,accuracy_score\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_excel('北京市空气质量数据.xlsx')\n",
    "data=data.replace(0,np.NaN)\n",
    "data=data.dropna()\n",
    "X=data.iloc[:,3:-1]\n",
    "Y=data['质量等级']\n",
    "Y=Y.map({'优':'1','良':'2','轻度污染':'3','中度污染':'4','重度污染':'5','严重污染':'6'})\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y,train_size=0.70, random_state=123)\n",
    "\n",
    "B=300\n",
    "dt_stump = tree.DecisionTreeClassifier(max_depth=3, min_samples_leaf=1)\n",
    "TestErrAdaB=np.zeros((B,))\n",
    "adaBoost = ensemble.AdaBoostClassifier(base_estimator=dt_stump,n_estimators=B,random_state=123)\n",
    "adaBoost.fit(X_train,Y_train)\n",
    "for b,Y_pred in enumerate(adaBoost.staged_predict(X_test)):\n",
    "    TestErrAdaB[b]=zero_one_loss(Y_test,Y_pred) \n",
    "\n",
    "TestErrGBRT=np.zeros((B,))\n",
    "GBRT=ensemble.GradientBoostingClassifier(loss='deviance',n_estimators=B,max_depth=3,min_samples_leaf=1,random_state=123)\n",
    "GBRT.fit(X_train,Y_train)\n",
    "for b,Y_pred in enumerate(GBRT.staged_predict(X_test)):\n",
    "    TestErrGBRT[b]=zero_one_loss(Y_test,Y_pred) \n",
    "\n",
    "TestErrBag=np.zeros((B,)) \n",
    "TestErrRF=np.zeros((B,))\n",
    "for b in np.arange(B):\n",
    "    Bag=ensemble.BaggingClassifier(base_estimator=dt_stump,n_estimators=b+1,oob_score=True,random_state=123,bootstrap=True)\n",
    "    Bag.fit(X_train,Y_train)\n",
    "    TestErrBag[b]=1-Bag.score(X_test,Y_test)\n",
    "    RF=ensemble.RandomForestClassifier(max_depth=3,n_estimators=b+1,oob_score=True,random_state=123,\n",
    "                                       bootstrap=True,max_features=\"auto\")\n",
    "    RF.fit(X_train,Y_train)\n",
    "    TestErrRF[b]=1-RF.score(X_test,Y_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x190f2bae4c8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "plt.plot(np.arange(B),TestErrAdaB,linestyle='--',label=\"AdaBoost分类树\",linewidth=1)\n",
    "plt.plot(np.arange(B),TestErrGBRT,linestyle='-',label=\"梯度提升分类树\",linewidth=2)\n",
    "plt.plot(np.arange(B),TestErrBag,linestyle='-.',label=\"Bagging分类树\",linewidth=2)\n",
    "plt.plot(np.arange(B),TestErrRF,linestyle='-.',label=\"随机森林分类\",linewidth=1)\n",
    "plt.title(\"空气质量等级的预测\",fontsize=12)\n",
    "plt.xlabel(\"迭代次数\",fontsize=12)\n",
    "plt.ylabel(\"测试误差\",fontsize=12)\n",
    "plt.legend()   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "说明：基于空气质量监测数据，采用集成学习的不同策略预测空气质量等级，并进行对比。 1、首先利用旁置法划分训练集和测试集。 2、以树深度等于3的分类树作为集成学习器，分别建立基于提升策略的分类树和梯度提升分类树，并计算迭代次数增加至300过程中的测试误差。 3、分别采用袋装策略和随机森林建立分类模型，并计算迭代次数增加至300过程中的测试误差。 4、对计算结果绘制折线图。图形显示，梯度提升树的测试误差最低，预测效果最为理想。"
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      "[232]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.22893\n",
      "[233]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[234]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[235]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23688\n",
      "[236]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23370\n",
      "[237]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23370\n",
      "[238]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[239]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23370\n",
      "[240]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23370\n",
      "[241]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23370\n",
      "[242]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23529\n",
      "[243]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23529\n",
      "[244]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23529\n",
      "[245]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23688\n",
      "[246]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23529\n",
      "[247]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23370\n",
      "[248]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23688\n",
      "[249]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23688\n",
      "[250]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[251]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[252]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[253]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[254]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[255]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[256]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[257]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[258]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[259]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[260]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[261]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[262]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[263]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[264]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[265]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23370\n",
      "[266]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23370\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[267]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[268]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[269]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[270]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[271]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[272]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[273]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[274]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[275]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23370\n",
      "[276]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[277]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[278]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[279]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[280]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[281]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[282]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[283]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[284]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[285]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[286]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[287]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[288]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[289]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[290]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[291]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[292]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[293]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[294]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[295]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[296]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[297]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n",
      "[298]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23053\n",
      "[299]\tvalidation_0-merror:0.00000\tvalidation_1-merror:0.23211\n"
     ]
    }
   ],
   "source": [
    "Xtrain=np.array(X_train)\n",
    "Ytrain=np.array(Y_train)\n",
    "Xtest=np.array(X_test)\n",
    "Ytest=np.array(Y_test)\n",
    "modelXGB=xgb.XGBClassifier(max_depth=3,learning_rate=1, n_estimators=B,objective='multi:softmax',random_state=123)\n",
    "modelXGB.fit(Xtrain,Ytrain,eval_set=[(Xtrain, Ytrain), (Xtest, Ytest)],verbose=True)\n",
    "result=modelXGB.evals_result()\n",
    "#print(accuracy_score(Ytrain,modelXGB.predict(Xtrain)))\n",
    "#print(1-zero_one_loss(Ytest,modelXGB.predict(Xtest)))   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x190f433a8c8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(np.arange(B),TestErrAdaB,linestyle='--',label=\"AdaBoost分类树\",linewidth=1)\n",
    "plt.plot(np.arange(B),TestErrGBRT,linestyle='-',label=\"梯度提升分类树\",linewidth=2)\n",
    "plt.plot(np.arange(B),TestErrBag,linestyle='-.',label=\"Bagging分类树\",linewidth=2)\n",
    "plt.plot(np.arange(B),TestErrRF,linestyle='-.',label=\"随机森林分类\",linewidth=1)\n",
    "plt.plot(np.arange(B),result['validation_1']['merror'],linestyle='-',label=\"XGBoost分类\",linewidth=2)\n",
    "plt.title(\"空气质量等级的预测\",fontsize=15)\n",
    "plt.xlabel(\"迭代次数\",fontsize=15)\n",
    "plt.ylabel(\"测试误差\",fontsize=15)\n",
    "plt.legend()   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x190f43dc708>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IfoM+IqpAtY/plZ3dppmZ7Zohh72kqcB84O3AByOirwejmZlZCxr0cQnFjVDXAt8Efggc7qA3MxtZBnvE8UHA3aSvIwzg94GROWjbzCxjgx3ZXw28JyI+ERFHk26u+n7P4w/MzGxkGKzP/oSIeKHnTURcX9xRe6OkaRFxZbnFMzOzehjsawlf6GPa05LeBtwj6emIuL600pmZWV0M5Xn2SDpV0r4974ubq94DrOp/KTMzaxWDDr2UNAV4PzBB0lvZ8ZhjgL2Ad5RUNjMzq5PBRuN0ANeQRuNcC0wCxhc/+wGXlVw+MzOrg4G+qWo6sBz4M+CzwJHAV0hfYtLzNLO9yi2emZnVw0BH9huAfwMuInXdnAr0fFVhpZjnlDILZ2Zm9dHvkX1EbADmSToOWAS8mvQFJFOBF4F9+fX+ezMza1FDeTZOJ3ApcDTwzxHxA0kn13zVoJmZtbh+u3EkjZJ0IWm0zfeBQ4DrJH0NuELSLZJublA5zcxsFwx0ZD8G2AjsD3yjeP0ocAXpi8cPiYglpZfQzMx22UB99puBv5f03xFxh6Q9gXOBbuBBYHODymhmZrto0D77iLij+P0r0hDMHtWSymRmZnU2pMclmJnZyOawNzPLgMPezCwDDnszsww47M3MMuCwNzPLgMPezCwDDnszsww47M3MMuCwNzPLwFAecVwXkvYBZgMPR8Qzg82/acs2pi+8tfyCtagFnVs5u6b+6xbPbWJpzGykK+3IXtIESd+VVJV0MvBt4Cjge5Iml7VdMzN7pTK7cWYBKyOiAvwS+MuI+DhwJ3BEids1M7NeSunGkTQfOAeYKOkY4PSIeFrSG0lH9x8rY7tmZtY3RUQ5K5YqQCUiFhXvRXpE8quBd0fEpj6WmQfMA5g0afLsS5deXUrZRoIpY+HJmhbqnDqheYVpku7ubjo6OppdjKbJvf7gNhhO/bu6ulZHxJze0xt2gTbSXuUCSZcB7wT+sY95lgHLAKbNmBlL1jSseC1nQedWauu/7sxK8wrTJNVqlUql0uxiNE3u9Qe3QT3r35Chl5L+WtJZxduJwIZGbNfMzJJGHTovA26QdB7pe2xXDLbA2PY21mY83LBarWZ5NG9m5Sgt7COiSvHVhRHxLPDmsrZlZmYD8x20ZmYZcNibmWXAYW9mlgGHvZlZBhz2ZmYZcNibmWXAYW9mlgGHvZlZBhz2ZmYZcNibmWXAYW9mlgGHvZlZBhz2ZmYZcNibmWXAYW9mlgGHvZlZBhz2ZmYZcNibmWXAYW9mlgGHvZlZBhz2ZmYZcNibmWXAYW9mlgGHvZlZBhz2ZmYZcNibmWXAYW9mloHRzS5AfzZt2cb0hbc2uxgNtW7x3GYXwcx2U6WFvaQJwI2ks4flwLuBNmAjcEZEvFTWts3M7NeV2Y0zC1gZERVgHPDpiHgL8ATwthK3a2ZmvZRyZC9pPnAOMFHSMcDpEfF08fFk4KkytmtmZn1TRJSzYqkCVCJiUc20o4HLI+IP+llmHjAPYNKkybMvXXp1KWVrVZ1TJ7z8uru7m46OjiaWpvlyb4Pc6w9ug+HUv6ura3VEzOk9vWEXaCXtA3wGOLW/eSJiGbAMYNqMmbFkTctePy7FujMrL7+uVqtUKpV+581B7m2Qe/3BbVDP+jdk6KWkMcDXgYsj4vFGbNPMzHZo1Dj7/wMcAfyNpKqkMxq0XTMzo8RunIioAtXi9eeBz+/M8mPb21jrcedmZnXhO2jNzDLgsDczy4DD3swsAw57M7MMOOzNzDLgsDczy4DD3swsAw57M7MMOOzNzDLgsDczy4DD3swsAw57M7MMOOzNzDLgsDczy4DD3swsAw57M7MMOOzNzDLgsDczy4DD3swsAw57M7MMOOzNzDLgsDczy4DD3swsAw57M7MMOOzNzDLgsDczy4DD3swsAw57M7MMOOzNzDLgsDczy4DD3swsA4qIZpehT5JeANY2uxxNNAl4ptmFaLLc2yD3+oPbYDj1f01ETO49cXR9ylOKtRExp9mFaBZJq3KuP7gNcq8/uA3qWX9345iZZcBhb2aWgVYO+2XNLkCT5V5/cBvkXn9wG9St/i17gdbMzOqnlY/szcysThz2ZmYZcNibmWWg6WEv6YuS7pf04V2ZZ6QarG6SJki6XdIKSTdKGtPoMpZtqP++kqZIerhR5WqUnaj/VZJOaFS5GmkI/w/2lnSbpFWSvtDo8pWt+Nu+d4DP2yXdIuk+SecOZxtNDXtJpwBtEXE0MEPSQcOZZ6QaYt3OBD4dEW8BngDe1sgylm0n/30/BYxtTMkaY6j1l3QssH9E3NLQAjbAENvgT4GvFjcYjZe029xoJWlv4EvAuAFmuxBYHRFvAE6TNH5nt9PsI/sKcEPxegVwzDDnGakqDFK3iLgqIu4q3k4GnmpM0RqmwhD+fSW9CdhI2uHtTioMUn9J7cDVwDpJJzauaA1TYfC/gV8Ah0qaCBwI/FdjitYQ24AzgOcHmKfCjja6B9jpnV2zw34csL54/UtgyjDnGamGXDdJRwN7R8QDjShYAw3aBkXX1SXAwgaWq1GG8jdwFvD/gU8CR0m6sEFla5ShtMH3gdcA7wN+XMy3W4iI5yPiuUFm2+UcbHbYd7PjtLyDvsszlHlGqiHVTdI+wGeAYfXVtbihtMFC4KqI2NCwUjXOUOp/OLAsIp4ArgO6GlS2RhlKG3wEOD8iPgY8BpzToLK1il3OwWYH52p2nLLNAtYNc56RatC6FUe1XwcujojHG1e0hhnKv+/xwAWSqsBhkq5pTNEaYij1/ykwo3g9B9jd/g6G0gZ7A52S2oDfA3K7G3TXczAimvYD7AU8AnyadGo2C7h8kHkmNLPMTaj/nwPPAtXi54xml7vRbdBr/mqzy9yEv4HxpB3+PcD9wNRml7sJbXAU8K+kI9y7gI5ml7uEdqgWv98EvLfXZ68p6n8l8CDpgvZOrb/pj0sorkS/Gbgn0mnqsOYZqXbnug1V7m2Qe/3BbTAUkg4gHd3fGYP38b9y+WaHvZmZla/ZffZmZtYADnszsww47C0rkv6w6B9G0jRJR/Uz3zhJM/r6rI959y36U5H0O8WNP7WfHyDpHTXvT5d04PBrYbbzHPaWDUkCPg7sL+kw4LXA+ZIOK372l7SnpEOBPwY+IunQmp+OmnUdL+mK4u2+pJEkkG7+mtpr0y8An5S0RzGU9kPk/SXa1gS+QGvZKI6u30m6Jf81wHbSeG0BbcADpFvXP0wa6ljrJOAzEXF3sa5jSOP/PwN8k3SH40agE1gDzCc92uIbwIvAGGBzMd+rgH8H2oELI+KRUipsVmN0swtg1kAXAw+Tbr0/H/gVKfBHAVsj4lOSjiQFdu+HUk0n7Qgobuzp0Q48DVwEXA78EXAKML4I8TcUy5wMfLtY5viIuL3elTMbiMPesiDpLOA/ircbgMXA1r5mBR4F/qnX9HfWvL4IOI3UfTO6+Lmk+Oz9pBuDerYrYFEx/6OkLp0LJF0GXAbcHD69tgZw2Fsu7iDdcv7npL/7/UlH6j3dOD3B3w4cCuzZa/npxXJExBJJPyB143yOtON4ADiOdJdrJ/CCpP1ITypcAxweES8V63pH8cjiS4DvknYAZqVy2FsWIuIpST0B/hJwXq9ZfgZ8PSLuA6YBSBpFetLoL4r3bb2WISKelvQL0iNqAd4FvBgRjxTL30B6kNsbJP0GqdtoG6mb6JyIcNBbQzjsLUdbSX30x/dMkHRr8fsS4KukETPLSRda31UM0VwAnCFpHDAbOKV4Iul+wP8jXYClWJaI2C5pBfA6Un/99GKebtLZw5Zyq2m2g8PectLz9x7A4ZK+U/PZnsXF2enF+zERcZ+kFyQdTOqff1bS6aRwnwCcHhFrJX2NHX33kLqFzFqKw95y0kEaAtkOPNzryH4FqYvlSuA54I3FI5UhfUPYpaSj/C0Rsb3XekcDF1CM1qH4YoniLOEE0o7iWGCPYhvbSc8mP1XSnRGxO34pi7UYj7M3M8uA76A1M8uAw97MLAMOezOzDDjszcwy4LA3M8vA/wKKQSkQ/NDQhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xgb.plot_importance(modelXGB,title=\"输入变量重要性\",ylabel=\"输入变量\",xlabel=\"分组次数\",importance_type=\"weight\",show_values=False)\n",
    "xgb.plot_importance(modelXGB,title=\"输入变量重要性\",ylabel=\"输入变量\",xlabel=\"信息增益\",importance_type=\"gain\",show_values=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
